Given the difficulty of reversing obesity once present, there has been increasing focus on the primary prevention of obesity early in the lifecourse. Few attempts have been made to prevent obesity during the first years of life. This proposal summarizes a 5-year program of mentored professional development tied to a multi-method research project intended to improve the identification and potential treatment of infants and toddlers with high risk for obesity. My long-term goal is to prevent obesity by identifying infants at greatest risk and providing for them an effective, family-centered intervention that targets modifiable, life course factors. My central hypothesis, based on my prior research, is that identifying infants at risk for obesity prior to the onset of unhealthy weight gain will enable early intervention. My research plan aims to: (1) create risk prediction models for obesity at age 24 months by linking three existing data systems that combine birth certificate, contextual- level, and health outcome data; (2) test the feasibility of linking these data prospectively to validate the Aim 1 obesity risk prediction models over a 24-month period within a contemporary, clinical cohort; and (3) identify best approaches for family-focused risk communication regarding the prevention of excessive weight gain and obesity in infants and toddlers using a human-centered design approach. Through my career development plan and guidance from my mentors, I will expand upon a foundation in epidemiology and pediatric health services research to develop expertise in machine learning, health informatics, data integration, qualitative methods, human-centered design, and behavior change. Together, the research and educational aims of this proposal will provide me with the necessary groundwork to compete for additional funding as an independent investigator. Specifically, I will seek R03-level grant funding from the NIDDK in year 4 of my K01 award to test the communication strategy developed in Aim 3 and to partner with families to modify an existing behavioral change intervention for use in infancy. By the end of this award, I will be well positioned to apply for funding from the NIDDK to conduct a robust R01-level study that combines the validated prediction models, family- focused communication strategy, and modified intervention to determine whether we can effectively prevent obesity in those infants and toddlers identified as being at the highest risk. This line of research will help ensure that prevention efforts are deployed in an efficient, cost-effective manner and accepted by those who need them. I will accomplish this work under the mentorship of Dr. Aaron E. Carroll, a child health services researcher, and a multidisciplinary team of faculty with expertise machine learning, health informatics, data integration and surveillance, qualitative research, human-centered design approaches, behavior change, and childhood obesity. I am ideally suited to complete this research due to my past research productivity, current mentorship team, open access to health care data, and the established clinical decision support infrastructure at the Indiana University School of Medicine.